Marmoset core visual object recognition behavior is comparable to that of macaques and humans.

Among the smallest simian primates, the common marmoset offers promise as an experimentally tractable primate model for neuroscience with translational potential to humans. However, given its exceedingly small brain and body, the gap in perceptual and cognitive abilities between marmosets and humans requires study. Here, we performed a comparison of marmoset behavior to that of three other species in the domain of high-level vision. We first found that marmosets outperformed rats - a marmoset-sized rodent - on a simple recognition task, with marmosets robustly recognizing objects across views. On a more challenging invariant object recognition task used previously in humans, marmosets also achieved high performance. Notably, across hundreds of images, marmosets' image-by-image behavior was highly similar to that of humans - nearly as human-like as macaque behavior. Thus, core aspects of visual perception are conserved across monkeys and humans, and marmosets present salient behavioral advantages over other small model organisms for visual neuroscience.

Brain-like functional specialization emerges spontaneously in deep neural networks.

The human brain contains multiple regions with distinct, often highly specialized functions, from recognizing faces to understanding language to thinking about what others are thinking. However, it remains unclear why the cortex exhibits this high degree of functional specialization in the first place. Here, we consider the case of face perception using artificial neural networks to test the hypothesis that functional segregation of face recognition in the brain reflects a computational optimization for the broader problem of visual recognition of faces and other visual categories. We find that networks trained on object recognition perform poorly on face recognition and vice versa and that networks optimized for both tasks spontaneously segregate themselves into separate systems for faces and objects. We then show functional segregation to varying degrees for other visual categories, revealing a widespread tendency for optimization (without built-in task-specific inductive biases) to lead to functional specialization in machines and, we conjecture, also brains.

Reduced Language Lateralization in Autism and the Broader Autism Phenotype as Assessed with Robust Individual-Subjects Analyses.

One of the few replicated functional brain differences between individuals with autism spectrum disorders (ASD) and neurotypical (NT) controls is reduced language lateralization. However, most prior reports relied on comparisons of group-level activation maps or functional markers that had not been validated at the individual-subject level, and/or used tasks that do not isolate language processing from other cognitive processes, complicating interpretation. Furthermore, few prior studies have examined functional responses in other brain networks, as needed to determine the spatial selectivity of the effect. Using functional magnetic resonance imaging (fMRI), we compared language lateralization between 28 adult ASD participants and carefully pairwise-matched controls, with the language regions defined individually using a well-validated language "localizer" task. Across two language comprehension paradigms, ASD participants showed less lateralized responses due to stronger right hemisphere activity. Furthermore, this effect did not stem from a ubiquitous reduction in lateralization of function across the brain: ASD participants did not differ from controls in the lateralization of two other large-scale networks-the Theory of Mind network and the Multiple Demand network. Finally, in an exploratory study, we tested whether reduced language lateralization may also be present in NT individuals with high autism-like traits. Indeed, autistic trait load in a large set of NT participants (n = 189) was associated with less lateralized language responses. These results suggest that reduced language lateralization is robustly associated with autism and, to some extent, with autism-like traits in the general population, and this lateralization reduction appears to be restricted to the language system. LAY SUMMARY: How do brains of individuals with autism spectrum disorders (ASD) differ from those of neurotypical (NT) controls? One of the most consistently reported differences is the reduction of lateralization during language processing in individuals with ASD. However, most prior studies have used methods that made this finding difficult to interpret, and perhaps even artifactual. Using robust individual-level markers of lateralization, we found that indeed, ASD individuals show reduced lateralization for language due to stronger right-hemisphere activity. We further show that this reduction is not due to a general reduction of lateralization of function across the brain. Finally, we show that greater autistic trait load is associated with less lateralized language responses in the NT population. These results suggest that reduced language lateralization is robustly associated with autism and, to some extent, with autism-like traits in the general population. Autism Res 2020, 13: 1746-1761. © 2020 International Society for Autism Research and Wiley Periodicals LLC.

Invariance to background noise as a signature of non-primary auditory cortex.

Despite well-established anatomical differences between primary and non-primary auditory cortex, the associated representational transformations have remained elusive. Here we show that primary and non-primary auditory cortex are differentiated by their invariance to real-world background noise. We measured fMRI responses to natural sounds presented in isolation and in real-world noise, quantifying invariance as the correlation between the two responses for individual voxels. Non-primary areas were substantially more noise-invariant than primary areas. This primary-nonprimary difference occurred both for speech and non-speech sounds and was unaffected by a concurrent demanding visual task, suggesting that the observed invariance is not specific to speech processing and is robust to inattention. The difference was most pronounced for real-world background noise-both primary and non-primary areas were relatively robust to simple types of synthetic noise. Our results suggest a general representational transformation between auditory cortical stages, illustrating a representational consequence of hierarchical organization in the auditory system.

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems.

Listening Into 2030 Workshop: An Experiment in Envisioning the Future of Hearing and Communication Science.

Here we report the methods and output of a workshop examining possible futures of speech and hearing science out to 2030. Using a design thinking approach, a range of human-centered problems in communication were identified that could provide the motivation for a wide range of research. Nine main research programs were distilled and are summarized: (a) measuring brain and other physiological parameters, (b) auditory and multimodal displays of information, (c) auditory scene analysis, (d) enabling and understanding shared auditory virtual spaces, (e) holistic approaches to health management and hearing impairment, (f) universal access to evolving and individualized technologies, (g) biological intervention for hearing dysfunction, (h) understanding the psychosocial interactions with technology and other humans as mediated by technology, and (i) the impact of changing models of security and privacy. The design thinking approach attempted to link the judged level of importance of different research areas to the "end in mind" through empathy for the real-life problems embodied in the personas created during the workshop.